Updated: Feb 26
Those who follow my content know that I'm on a mission to make everyone more AI-aware. I'm grateful for the buzz that's been generated by ChatGPT as its awakened our collective imagination for the art of the possible! With the rapid advancement of AI, it's changing the way we work and make decisions. Predictive AI has gained popularity due to its ability to analyze data and predict future outcomes - WHAT is likely to happen. However, another type of AI, known as causal AI, is also making its mark as it determines WHY things are happening.
In this article, I dig into the power of causal AI and how it is revolutionizing industries such as healthcare, financial services, and manufacturing. I also discuss how predictive AI and causal AI can work together to provide a more complete understanding of complex problems and outcomes.
What is Causal AI?
Causal AI is like a detective that helps us to solve a mystery. Just like a detective looks for evidence and clues to determine who or what caused a crime, causal AI looks for patterns and relationships in data to determine what factors cause a specific outcome. By using causal AI, we can identify the root cause of a problem and develop targeted solutions that address it.
How Does it Differ from Predictive AI?
Causal AI is distinct from predictive AI, which is far more commonly utilized today. While both identify patterns and relationships, predictive AI tells us WHAT is likely to happen by using machine learning algorithms to analyze data and make predictions about future outcomes. In contrast, causal AI goes beyond prediction by identifying the underlying causes of an outcome. Causal AI not only identifies correlations between variables but also determines the causal relationships between them, which allows us to understand WHY an outcome is happening.
For example, predictive AI might tell you that a certain group of customers is more likely to churn, while causal AI can help you understand the underlying factors that are causing the churn, such as poor customer service or a lack of product features. By identifying the causal relationships between variables, causal AI can help us to make targeted improvements to prevent negative outcomes and achieve better results.
Why Causal AI is Important
Causal AI is revolutionizing a wide range of industries with its ability to identify the root causes of problems - WHY something is happening. This makes it a powerful tool for machine+human decision-making and enables the creation of targeted solutions. To better illustrate the capabilities of this technology, below are analogies and examples for healthcare, financial services, and manufacturing that showcase the ways in which causal AI can solve real-world challenges.
Industry Analogies and Examples
Healthcare data is like a puzzle that needs to be solved. Just like a puzzle has many different pieces that need to be put together in the right order to create a complete picture, healthcare data has many different variables that need to be analyzed and understood in order to diagnose and treat disease. By using causal AI to analyze healthcare data, you can put the puzzle pieces together and create a complete picture of the patient's health, which can lead to better outcomes.
Let's say that a healthcare provider is seeing a high rate of readmissions for patients with heart failure. The provider has collected a large amount of data about the patients, including data on their demographics, medical history, and treatments.
To identify the root cause of the readmissions, the healthcare provider could use causal AI to analyze the data and identify the factors that are causing the readmissions. The causal AI algorithm would be able to identify the variables that are most strongly associated with readmissions and determine which variables are actually causing the problem.
For example, the algorithm might identify that a particular medication is not effective for a certain subset of patients with heart failure, or that a particular treatment plan is associated with a higher rate of readmissions. By identifying the root cause of the problem, the healthcare provider can take targeted action to address the issue and reduce the rate of readmissions.
Using causal AI in healthcare can help to optimize patient care, improve health outcomes, and reduce healthcare costs. By putting the puzzle pieces together and identifying the underlying causal relationships between variables, healthcare providers can make better decisions and provide more effective treatments for their patients.
Financial data is like a trail of breadcrumbs that leads to the truth. Just like a trail of breadcrumbs can lead you to a hidden treasure, financial data can lead you to the truth about credit risk, fraud, and other financial crimes. By using causal AI to analyze financial data, you can follow the trail of breadcrumbs and uncover the truth about what caused a specific financial event.
Let's say that a financial institution is experiencing a high rate of credit card fraud. The institution has collected a large amount of data about the transactions that have taken place, including data on the cardholder, the merchant, the location, and the time of the transaction.
To identify the root cause of the fraud, the financial institution could use causal AI to analyze the data and identify the factors that are causing the fraudulent transactions. The causal AI algorithm would be able to identify the variables that are most strongly associated with fraudulent transactions and determine which variables are actually causing the fraud.
For example, the algorithm might identify that a particular merchant is associated with a high number of fraudulent transactions, or that a particular type of transaction is more likely to be fraudulent. By identifying the root cause of the fraud, the financial institution can take targeted action to address the problem and reduce the incidence of fraud.
Using causal AI in financial services can help to optimize risk management, reduce losses due to fraud, and improve customer satisfaction. By following the trail of breadcrumbs in financial data and identifying the underlying causal relationships between variables, financial institutions can make better decisions and improve their bottom line.
Manufacturing is like a symphony that needs to be conducted. Just as a symphony requires many different instruments played in harmony, manufacturing requires many different parts and processes working together seamlessly. Like a conductor who oversees and fine-tunes each section of the orchestra, a manufacturer must oversee and optimize each aspect of the manufacturing process to achieve the desired outcome. By using causal AI to analyze manufacturing data, you can conduct the manufacturing symphony with precision to eliminate defects and ensure consistently high-quality output.
Let's say that a manufacturer is experiencing quality issues with a particular product. The product has a defect rate of 5%, which is too high for the manufacturer to meet their customer's quality standards. The manufacturer has collected a large amount of data about the production process, including data on the raw materials used, the production line, and the workers involved.
To identify the root cause of the quality issues, the manufacturer could use causal AI to analyze the data and identify the factors that are causing the defects. The causal AI algorithm would be able to identify the variables that are most strongly associated with the presence of defects and determine which variables are actually causing the defects.
For example, the algorithm might identify that a particular machine on the production line is causing defects due to a malfunctioning component. Or it might find that a particular worker is consistently making errors that are leading to defects. By identifying the root cause of the defects, the manufacturer can take targeted action to address the problem and eliminate the defects.
Using causal AI in manufacturing can help to optimize the production process, reduce waste, and improve product quality. By identifying the root causes of defects, manufacturers can make targeted improvements that lead to better outcomes, lower costs, and higher customer satisfaction just as a symphony performed with precision and harmony can delight its audience.
Powerhouse Combo: Predictive AI + Causal AI
Predictive AI and causal AI can be used together to create a more complete understanding of complex problems and outcomes. Predictive AI can be used to identify patterns and relationships in data and make predictions (WHAT) about future outcomes, while causal AI can be used to identify the underlying causes (WHY) of those outcomes. By using both types of AI in tandem, we can gain a more comprehensive understanding of the factors that are driving a particular outcome, and use that knowledge to develop targeted solutions to address the problem.
For example, let's say that a retailer is experiencing a high rate of product returns, and they want to understand why. Predictive AI could be used to identify which products are most likely to be returned based on customer demographics, purchase history, and other factors. Causal AI could then be used to identify the underlying factors that are causing those returns, such as poor product quality or a lack of product information. By using both predictive and causal AI, the retailer can gain a more complete understanding of the problem and develop targeted solutions to reduce returns and improve customer satisfaction.
Causal AI is a powerful tool that is transforming various industries by identifying the root cause - the WHY - of problems. Predictive AI and causal AI can act as a powerhouse duo by working together to provide a comprehensive understanding of complex issues. Armed with this understanding, machine+human decision-making can craft precise solutions. With the potential to optimize a multitude of industries, causal AI is an exciting arena!